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Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios
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Chapter 12: Applications of Database Marketing in B-to-C and B-to-B Scenarios

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  • 1. Customer Relationship Management A Databased Approach V. Kumar Werner J. Reinartz Instructor’s Presentation Slides
  • 2. Chapter Twelve Applications of Database Marketing in B-to-C and B-to-B Scenarios
  • 3. Topics Discussed
    • Customer Value- A Decision Metric
    • Study 1: The Life-time-Profitability Relationship in a Non-contractual Setting
    • Study 2: A model for incorporating customers’ projected profitability into lifetime duration computation
    • Study 3: A model for identifying the true value of a lost customer
  • 4. Study 1:The Life-time-Profitability Relationship in a Non-contractual Setting
    • Background and Objective
      • The firm has to ensure that the relationship stays alive since the customer typically splits his/her category expenses with several firms
    • Objectives:
      • Test for the strength of the lifetime duration – profitability relationship
      • Whether profits increase over time (lifetime profitability pattern)
      • Whether the costs of serving long-life customers are actually less
      • Whether long-life customers pay higher prices
  • 5. Study 1 (contd.)
    • Conceptual Model
    • To investigate the consequences of customer retention, namely, profitability:
      • Individual customer lifetime profits are modeled as a function of a customer’s lifetime duration
      • Revenue flows over the course of a customer’s lifetime
      • Firm cost is associated with the marketing exchange
  • 6. Customer Lifetime and Firm Profitability:
    • Proposition 1:The Nature of the Lifetime-Profitability Relationship is Positive
    • Lifetime-Profitability Association Segmentation Scheme
    Lifetime Profit Low High Lifetime revenue High Low Short Long Lifetime duration Segment 2 Segment 3 Segment 4 Segment 1 Long Lifetime Duration Short
  • 7. Customer Lifetime and Firm Profitability : Proposition 2: Profits Increase over Time
    • Analysis of the dynamic aspects of the lifetime-profitability relationship
    • Non-contractual setting: cost of serving customer can easily exceed the profit margin brought in by the customer. Therefore, profits may not increase over time
    • Example: catalog shopping or direct mail offerings - the customer may end up buying once a year and spend a smaller amount
  • 8. Customer Lifetime and Firm Profitability:
    • Whether there is lower transaction costs for longer-life versus shorter-life customers (e.g.: retail sector)
    • Whether the costs associated with promotional expenditures directed at longer- and shorter-life customers actually differ
    Proposition 3: The Costs of Serving Longer-life Customers are lower
  • 9. Customer Lifetime and Firm Profitability:
    • Proposition 4: Longer-life Customers Pay Higher Prices
    • Existing customers pay effectively higher prices than new ones, even after accounting for possible introductory offers
      • OR
    • Higher value consciousness of long-term customers because customers learn over time to trust lower priced items or brands rather than established name brand products
  • 10.
    • Model for Measuring Customer Lifetime for Non-Contractual Relationships
      • Negative Binomial Distribution (NBD)/Pareto model
      • Based on the customer-specific probability of being alive, the model can be used to determine which customers should be deleted from active status
      • Given that the outcome of the NBD/Pareto model is a continuous probability estimate, continuous P(Alive) estimate is transformed into a dichotomous “alive/dead” measure
      • Knowing a person’s “time of birth” and given a specified probability level threshold), can approximate when a customer is deemed to have left the relationship
      • The time from birth, t 0 , until the date associated with the cut-off threshold, t cut-off , constitutes the lifetime of the customer
      • Calculate finite lifetime for each customer, for profitability analysis
    Research Methodology
  • 11. Illustrative Lifetime Determination of Individual Household Source: Reinartz W. And Kumar V., “On the Profitability of Long –life Customers in a Non-Contractual Setting: An empirical Investigation and Implications for Marketing
  • 12. Measuring Customer Lifetime for Non-Contractual Relationships (contd.)
    • Establishment of Cut-off Threshold
      • E stablishment of cut-off threshold that produces the highest percentage of correct classifications (here 0.5)
    • Lifetime Estimation
    • Finite Lifetime Estimates
    35 12 41.7 7.9 27.9 Cohort 2 36 11 41.1 7.8 28.7 Cohort 1 Maximum Minimum % Right- Censored Standard deviation Mean Lifetime (months)
  • 13. Measuring Customer Lifetime for Non-Contractual Relationships (contd.)
    • Profit Calculation
      • Net-present value of profit is calculated on an individual customer basis for the period of 36 months as:
    • where LT  i = individual net-present lifetime profit for 36 months
    • GCti = gross contribution in month t for customer i ,
    • Cti = mailing cost in month t for customer i ,
    • 0.0125 = monthly discount rate (based on 0.15 rate per year)
  • 14. Test of the Propositions
    • Proposition 1:
      • Could customers with shorter tenure might actually be more profitable than long-term customers, a claim that runs counter to the theoretical expectations of a relationship perspective?
      • Which group of customers is of more interest to the firm, the one that buys heavily for a short period or the one with small spending but with long-term commitment?
  • 15. Test of the Propositions (contd.)
    • Proposition 2
      • Examine the profitability evolution visually
      • Analyze the sign of the slope coefficient
      • The exact specification of the regression is:
    • Profits = a s + b 1s *Dummy + b 2s *t s + error
    • where t = month, b is = regression coefficient, s =segment,
    • Dummy = 1 if first purchase month, else 0
    • Proposition 3
      • Compute the ratio of promotional costs in a given period over the revenues in the same period
    • Proposition 4
      • Whether longer-life customers do pay higher prices as compared to shorter-life customers
  • 16. Tests of Propositions - Results (Cohort 2 results in parentheses) High Lifetime Revenue Low Lifetime Revenue 63.54 (64.47) 0.065 (0.064) 11.67 (12.57) 257.96 (284.20) 783 (942) 47.97 (46.80) 0.141 (0.143) 2.41 (2.67) 50.49 (53.67) 1208 (1504) Average Item Price Mailing Cost/ Sales Ratio Relative Profit ($/month) Lifetime Profit per Customer ($) # of customers Av: Item Price Mailing Cost/ Sales Ratio Relative Profit ($/month) Lifetime Profit per Customer ($) # of customers Short Life time Segment 3 Segment 4 58.43** (58.25)** 0.063* (0.062)* 8.18 (9.31) 289.83 (322.03) 1322 (1546) 47.74 (48.72) 0.128 (0.124) 1.43 (1.56) 50.85 (55.26) 889 (973) 5 ) Average Item Price 4) Mailing Cost/ Sales Ratio 3) Relative Profit ($/month) 2) Lifetime Profit per Customer ($) 1) # of customers 5) Av: Item Price 4) Mailing Cost/ Sales Ratio 3) Relative Profit ($/month) 2) Lifetime Profit per Customer ($) 1) # of customers Long Life Time Segment 1 Segment 2
  • 17. Interpretation of Results
    • Difference between Segment 1 and Segment 3 is not significant
    • ** Difference between Segment 1 and Segment 3 is significant at  = 0.05
    • Both long-term customers (Segment 1) and short-term customers
    • (Segment 3) constitute the core of the firm’s business
    • Relationship between lifetime and profits can be far from being positive and monotonic
    • In terms of profit per month, customers in Segment 3 are the most attractive of all
    • Segment 3 customers purchase with high-intensity, thus generating higher profits in a relatively shorter period of time
  • 18. Interpretation of Results (contd.)
    • The mean relative profit for each segment is significantly different from the other segment at least at  = 0.05 (using the multiple comparison test)
    • Segment 1 customers are the most desirable set for the firm –
    • represents the loyalty effect at its best
    • For Segment 3 customers (high revenue but short lifetime), there appears to be a good match between offerings and desires but their relationship duration may be complicated by moderating factors
    • Dissatisfaction might occur for Segment 4 whose customers spend the lowest amount
  • 19. Aggregate Profits ($) for Short-life Segments
  • 20. Aggregate Profits ($) for Long-life Segments
  • 21. Regression Analysis of Profits as a Function of Time
            • Dummy Coefficient Coefficient
    • Segment Intercept (a) for t =1 (b1) for t (b2) R2
    • 1 12.11 (12.73) 45.77 (46.38) -0.13 (-0.14) .85 (.85)
    • 2 n.s. (n.s.) 30.24 (30.91) 0.07 (0.071) .92 (.91)
    • 3 19.40 (20.9) 57.85 (58.29) -0.70 (-0.75) .95 (.94)
    • 4 3.25 (3.69) 29.53 (31.45) -0.14 (-0.15) .94 (.95)
    Regression Results for t = 1 to 36 Months (Cohort 1) Validation Results in Parentheses (Cohort 2)
    • With the exception of Segment 2, the coefficient for the linear effect has a negative sign – highlights the negative profit trend over time for the 3 segments
    • All the coefficients for time are significant at  =0.01
  • 22. Are the Costs of Serving Long-Life Customers Lower?
    • The ratio of mailing cost per dollar sales in the longer-life segment (Segment 1) is statistically not different from the mailing cost per dollar sales in the shorter-life segment (Segment 3)
    • In terms of cost efficiency, Segments 1 and 3 are the most attractive to the firm, although they have very different lifetime properties
    • The ratio of mailing cost and revenues – which is one measure of efficiency – need not necessarily be lower for long-life-customers
  • 23. Do Long-Life Customers Pay Higher Prices?
    • The average price per item for segment 3 is significantly (  = 0.05) different from (and greater than) that of segment 1
    • The highest average price paid for a single product item is in Segment 3, the short-life segment
    • The highly profitable short-term customers seem to be less
    • sensitive to the product’s price
    • The higher spending (average prices paid) by Segment 3 customers may be due to some other benefit sought by them
  • 24. Summary of Findings
    • A strong linear positive association between lifetime and profits does not necessarily exist
    • A static and a dynamic lifetime-profit analysis can exhibit a much differentiated picture: profitability can occur for the firm from high and low lifetime customers
    • Profits do not increase with increasing customer tenure: the cost of serving long-life customers is not lower
    • Long-life customers do not pay higher prices
  • 25. Study 2: A model for Incorporating Customers’ Projected Profitability into Lifetime Duration Computation
    • Background and objectives
      • Empirically measure lifetime duration for non-contractual customer-firm relationships, incorporating projected profits
      • Understand the structure of profitable relationships and test the factors that impact a customer’s profitable lifetime duration
      • Develop managerial implications for building and managing profitable relationship exchanges
  • 26. Conceptual Model of Profitable Customer Lifetime Earlier Study B-to-C context only Present Study B-to-C and B-to-B contexts Profitable Lifetime Duration Exchange characteristics Observed heterogeneity Customer Profitability Lifetime Duration Non-contractual setting Revenues Cost
  • 27.
    • Determine the contribution margin expected from each customer
    • in future periods based on the average of the contribution margins in the past
    • Determine for each future period, the probability that the customer will be alive and will transact with the firm
    • Combine these two components
    • Discount the expected contribution margin in each future period to its
    • Net Present Value (NPV) using the cost of capital applicable to the firm
      • If in a given month, cost of additional marketing efforts is greater than NPV, determine that the Profitable Lifetime Duration of the customer has ended
    Estimating Profitable Lifetime Duration
  • 28. Antecedents of Profitable Lifetime Duration
    • Predictor variables to incorporate into the model:
      • Exchange variables: e.g. Amount Purchased, Degree of Cross-buying, Degree of Focused-buying, Inter-purchase Time, Number of Product Returns, Ownership of Loyalty Instruments, and Mailing efforts undertaken by the firm
      • Customer Heterogeneity variables: e.g. Location and Income of the customer
    • Conceptually,
    • Profitable Lifetime = f (Exchange characteristics, Customer heterogeneity)
    • Duration
  • 29. Research Methodology
    • The customer-firm interaction of Cohort 1 households is tracked for a 36-month time period,
    • Cohort 2 for 35 and Cohort 3 for a 34-month time period
    • The households are sampled randomly from all households that started in January, February, and
    • March 1995 respectively
    • The number of purchases ranges from 1 to 46 across the sample with a median number of 5
    • purchases, the median interpurchase time is 117 days, and the median transaction amount is $91
    • for each purchase
    Cohort 1: 4202 observations Cohort 3: 2825 observations Cohort 2: 4965 observations Jan ‘95 Feb ‘95 Mar ‘95 Dec ‘97 Start Finish Database Structure for B-to-C Setting
  • 30. Determining Profitable Customer Lifetime Duration
    • Calculation of net present value (NPV) of expected contribution margin (ECM it ):
    NPV of ECM it = where ECM it = estimated expected contribution margin for a given month t , AMCM it = average contribution margin in month t based on all prior purchases since birth (updated dynamically), r = discount rate (15% on a yearly basis) i = the customer t = month for which NPV is estimated n = number of months beyond t , P( Alive) in = probability that customer i is alive in month n .
  • 31. Determining Profitable Customer Lifetime Duration (contd.)
    • Decision of relationship termination
      • Formally, if NPV of Expected CM it < Cost of Mailing, the firm would decide to
      • terminate the relationship
      • Decision rule incorporates the cost of mailings and an average flat contribution
      • margin before mailings of 25 percent
      • Discount rate is assumed to be 15% per year
    • Calculation of finite lifetime estimate
      • Based on decision of relationship termination, average lifetime across Cohort 1 is 29.3 months, Cohort 2 is 28.6 months, and Cohort 3 is 27.8 months
      • Variability in lifetime duration evidenced through
        • a wide range between lowest and highest lifetime estimate
        • the standard deviation of the lifetime estimate
        • the relatively small value of s in the NBD/Pareto model
  • 32. Analysis
    • In the proportional hazard model, the hazard rate hi(t) for individual i is assumed to take the form:
    • where
    • h 0 (t) is the baseline hazard rate
    • (x it  ) is the impact of the independent variables
    h i (t) = h 0 (t) e x it 
  • 33. Variables for Profitable Lifetime Model (-)Inverse U-shaped relationship for AIT and AIT 2 (Number of Days) 2 (Average Interpurchase Time it ) 2 (+) Inverse U-shaped relationship for AIT and AIT 2 Number of Days Average Interpurchase Time it Non-directional hypothesis Dummy: 1 = buys consistently in single dept. only 0 = all other Focus of Buying i (+) Number of departments shopped in Cross Buying it + Monthly spending level ($), moving average over 6 month period Purchase amount it ** Hypothesized Directional Impact on Profitable Lifetime Measured as Independent Variables Months Profitable Lifetime i * Measured as Dependent Variable
  • 34. Variables for Profitable Lifetime Model (contd.) No directional hypothesis Age of individual in years Age i (+) Scale from 1 to 9 where 1 is < $10,000 and 9 is > 150,000 Income i (-) Number of people in 2 digit zip code Population Density No directional hypothesis 1 = more than 50 % of purchases in softgoods, 0 = more than 50 % of purchases in hardgoods Product Category i (+) Number of mailings sent in last 6 months (= 1 season) since current t , exponential decay, one month lag Mailings it (+) Ownership of Charge card. Dummy variable, 1 = owns card, 0 = no card Loyalty Instrument i (-) Proportion of returns (of sales) Returns it
  • 35. Analysis (contd.)
    • The hazard of a lifetime event of a household i at time t is given as:
    • h i (t) = h 0 (t) EXP (β 1 Purchase Amount it + β 2 Cross Buying it + β 3 Focus of Buying i + β 4 Average Interpurchase Time it + β 5 (Average Interpurchase Time it ) 2 + γ 1 Returns it + γ 2 Loyalty Instrument i + γ 3 Mailings it + γ 4 Product Category i + δ 1 Population Density i + δ 2 Income i + δ 3 Age i )
  • 36. Summary of Results Supported However, the relationship is negative, indicating that buying in only a single department results in shorter lifetime duration Supported However, the relationship is negative, indicating that buying in only a single department results in shorter lifetime duration Related to the Focused Buying behavior exhibited by customers 2 b Partial Support Only the linear term is significant Supported Related to Average Interpurchase Time in an inverse U-shaped manner whereby intermediate AIT is associated with the longest profitable lifetime 3 Not Supported However, the interaction of Returns with Purchase amount variable is significant Not Supported However, the interaction of Returns with Purchase amount variable is significant Inversely related to the proportion of merchandise returned by the customers. 4 Supported Supported Positively related to degree of cross-buying behavior exhibited by customers 2 a Supported Supported Positively related to the customer’s spending level 1 B-to-B Setting Result B-to-C Setting Result Description Profitable Customer Lifetime Duration is: Hypothesis
  • 37. Summary of Results (contd.) Supported Supported Profitable Customer Lifetime Duration is positively related to the Income of the customer (B-to-C) or Income of the firm (B-to-B) 8 Not supported Supported Profitable Customer Lifetime Duration is higher for customers living in areas with lower population density (B-to-C) or businesses existing in lower population density (B-to-B) 7 Supported Supported Positively related to the number of mailing efforts of the company (B-to-C) or the number of contacts (B-to-B) 6 Supported Supported Positively related to the customer’s ownership of the company’s loyalty instrument (B-to-C) or the availability of line of credit (B-to-B) 5
  • 38. Estimation of P (Alive)
    • Method-of-moment approach
    • Estimate the four parameters of the NBD/Pareto model ( r,  , s,  ) with a Fortran routine with likelihood:
    • where M is a random sample of customers and customer i made X i = x i purchases in (0 ,T i ) with the last transaction time at t i
    • The larger the value of the shape parameter r the more homogeneous the population of customers in terms of purchase rate
    • The larger the value of the shape parameter s the more homogeneous the population of customers in terms of dropout rate
    • The concentration in dropout rates,  , depends on the parameter s only
  • 39. Estimation of P (Alive) (contd.)
    • Probability that a customer with a particular observed transaction history is still alive at time T since trial:
    • P [Alive | r,  , s,  , x, t, T] =
    • where a 1 = r+x+s, b 1 = s+1, c 1 = r+x+s+1, z 1 (y) = (  -  ) / (  +y),
    • F(a 1 , b 1 ; c 1 ; z 1 ) is the Gauss hyper geometric function,
    • r,  , s,  = model parameters,
    • x = number of purchases,
    • t = time since trial at which the most recent transaction occurred,
    • T = time since trial
  • 40. Study 3 – A Model for Identifying the True Value of a Lost Customer
    • Conceptual Background
      • The value of a lost customer depends upon whether:
        • the customer defects to a competing firm or
        • disadopts the product category
      • Defection: The firm loses direct sales that customer would have brought in
      • Disadoption: Customer stops purchasing from that product category altogether
        • Affects the long term profitability by :
          • The loss of direct sales
          • Indirect effects of word of mouth, imitation, and other social effects
  • 41. Modeling the Effects of Disadoption on the Value of a Lost Customer
    • Value of an average lost customer (VLC) is calculated as:
    • VLC =  VLC disadopter + (1-  ) VLC defectors
    • where  is the proportion of disadopters in a firm’s lost customers
    • Profit impact of a lost customer = sales estimate from new product growth model without disadoption – the sales estimate when the customer disadopts after certain time
  • 42. Modeling the Effects of Disadoption on the Value of a Lost Customer (contd.)
    • Key determinants of the value of a lost customer:
      • The time when customer disadopts has the largest impact on the value of the lost customer; earlier disadoption causes more loss of money
      • The external influence, p has a negative impact
      • The internal influence, q has a positive impact on penetration because higher q signifies stronger word of mouth
      • Discount rate has positive impact on the value of lost customer
  • 43. Summary
    • A study of the Life-time-Profitability relationship in a non-contractual setting highlights concern with the widespread assumption of a clear-cut positive lifetime-profitability relationship and underlines the importance of a differentiated analysis
    • The empirical evidence showed that (1) a strong linear positive association between lifetime and profits does not necessarily exist; (2) profits do not necessarily increase with increasing customer tenure, (3) the cost of serving long-life customers is not lower, and (4) long-life customers do not pay higher prices
    • The main drivers of customer’s profitable lifetime duration are classified as exchange characteristics and customer heterogeneity
    • The key determinants of the value of a lost customer are identified as disadoption time, external and internal influences and the discount rate
    • The disadoption time is found to have the maximum negative impact on the value
    • i.e., the earlier a customer disadopts, the higher is the value of the lost customer

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